import streamlit as st
import os
import glob
import re
import base64
import pytz
from urllib.parse import quote
from gradio_client import Client
from datetime import datetime

# 🌳🤖 AIKnowledgeTreeBuilder - Because every app needs a good costume!
Site_Name = 'AI Knowledge Tree Builder 📈🌿 Grow Smarter with Every Click'
title = "🌳✨AI Knowledge Tree Builder🛠️🤓"
helpURL = 'https://huggingface.co/spaces/awacke1/AIKnowledgeTreeBuilder/'
bugURL = 'https://huggingface.co/spaces/awacke1/AIKnowledgeTreeBuilder/'
icons = '🌳✨🛠️🤓'
st.set_page_config(
    page_title=title,
    page_icon=icons,
    layout="wide",
    initial_sidebar_state="auto",
    menu_items={
        'Get Help': helpURL,
        'Report a bug': bugURL,
        'About': title
    }
)


# Initialize session state variables
if 'selected_file' not in st.session_state:
    st.session_state.selected_file = None
if 'view_mode' not in st.session_state:
    st.session_state.view_mode = 'view'
if 'files' not in st.session_state:
    st.session_state.files = []



AITopicsToInnovate1="""
1. Major AI Industry Players 🌐
   1. Research Leaders 🎯
      - OpenAI: GPT-4 DALL-E Foundation Models 🔵
      - Google: PaLM Gemini LLMs 🟦
      - Anthropic: Claude Constitutional AI ⚡
      - Meta: LLaMA Open Source LLMs 👤
      - xAI: Grok Conversational AI 🤖
   
2. Technical AI Development 🛠️
   1. Architecture Advances 💫
      - Transformer Models Attention Mechanisms 🧠
      - Mixture of Experts MoE Architecture 🎪
      - Sparse Neural Networks 🕸️
      - Multi-modal LLM Systems 🌈
      - Flash Attention Optimization ⚔️
   
   2. Training Methodologies 📚
      - LLM Supervised Fine-tuning 👨‍🏫
      - RLHF Reward Models 🤝
      - Constitutional AI Training 📜
      - RLAIF Feedback Models 🔄
      - Synthetic Data LLM Training 🎲
      - Chain of Thought Prompting 🧩
      - Tree of Thoughts Reasoning 🌳
   
   3. Post-Training Implementation 🔧
      - Neural Network Distillation 🧪
      - LLM Quantization Methods 📊
      - Neural Network Pruning ✂️
      - Knowledge Distillation Transfer 📖
      - Few-shot LLM Learning 🎯

3. Mechanistic Interpretability 🔬
   1. Core Concepts 💡
      - Neural Network Growth Analysis 🌱
      - LLM Architecture Analysis 🏗️
      - Training Loss Optimization 🎨
      - Neural Network Analogies 🧬
   
   2. Technical Features 📐
      - LLM Linear Representations ➡️
      - Neural Vector Arithmetic 🔢
      - Neural Activation Patterns 🌊
      - LLM Feature Detection 🔍
      - Neural Sparse Autoencoders 🎭
   
   3. Network Analysis 🕵️
      - LLM Induction Heads 👀
      - Transformer Attention Analysis 🎪
      - Neural Circuit Analysis 🔌
      - LLM Feature Visualization 📈
      - Neural Concept Directions 🎳

4. Future AI Developments 🚀
   1. AGI Timeline ⏰
      - AGI Capability Projections 📅
      - Neural Hardware Scaling 💾
      - LLM Training Data Limits 📉
      - AI Compute Resources 🗺️
   
   2. Integration Fields 🎡
      - AI Biology Integration 🔮
      - AI Drug Discovery Systems 💊
      - AI Clinical Trial Analysis 🏥
      - AI Code Generation 🤹
      - AI Scientific Discovery 🧮

5. Industry Best Practices 💎
   1. AI Team Building 🏢
      - AI Talent Development 👥
      - AI Research Alignment 🎪
      - AI Team Scaling 📊
      - AI Research Culture 🌟
   
   2. AI Research Qualities 🎓
      - AI Research Methodology 🧭
      - AI Experimentation Protocols 🗝️
      - AI Innovation Thinking 💫
      - AI Testing Framework ⚖️
   
   3. AI Safety Standards 🛡️
      - LLM Behavioral Specifications 📋
      - AI Safety Guidelines 🎮
      - AI Ethics Framework ⛑️
      - AI Industry Standards 🤲

6. Emerging Research Areas 🔮
   1. Technical Focus 🎯
      - LLM Long Context Learning ⏳
      - LLM Multi-agent Interaction 👾
      - AI Evaluation Metrics 📌
      - Neural Interpretability Methods 🔭
   
   2. AI Applications 💼
      - AI Automated Research 🧫
      - AI Code Synthesis ⌨️
      - AI Biological Modeling 🧯
      - AI Medical Diagnostics 💉

7. Model Intelligence 🧿
   1. LLM System Development 🎪
      - LLM Prompt Engineering 📝
      - LLM Response Generation ♟️
      - LLM Behavioral Training 🎹
      - LLM Personality Development 🎪
   
   2. LLM User Interaction 🎭
      - LLM Autonomy Alignment 🎪
      - LLM Safety Boundaries 🔒
      - LLM Communication Patterns 🗣️
      - LLM Performance Tuning 🎢

"""




DarioAmodeiKnowledge="""
1. Major AI Industry Players 🌐
   1. Research Leaders 🎯
      - OpenAI: GPT-4 DALL-E 🔵
      - Google: PaLM Gemini 🟦
      - Anthropic: Claude ⚡
      - Meta: LLaMA 👤
      - xAI: Grok 🤖
   
2. Technical AI Development 🛠️
   1. Architecture Advances 💫
      - Transformer Models 🧠
      - Mixture of Experts 🎪
      - Sparse Architectures 🕸️
      - Multi-modal Models 🌈
      - Flash Attention ⚔️
   
   2. Training Methodologies 📚
      - Supervised Fine-tuning 👨‍🏫
      - RLHF Human Feedback 🤝
      - Constitutional AI 📜
      - RLAIF AI Feedback 🔄
      - Synthetic Data Generation 🎲
      - Chain of Thought 🧩
      - Tree of Thoughts 🌳
   
   3. Post-Training Implementation 🔧
      - Model Distillation 🧪
      - Quantization 📊
      - Pruning ✂️
      - Knowledge Distillation 📖
      - Few-shot Learning 🎯

3. Mechanistic Interpretability 🔬
   1. Core Concepts 💡
      - Neural Network Growth Patterns 🌱
      - Architecture Scaffolding 🏗️
      - Training Objective Guidance 🎨
      - Biological System Analogies 🧬
   
   2. Technical Features 📐
      - Linear Representations ➡️
      - Vector Arithmetic 🔢
      - Activation Patterns 🌊
      - Feature Detection 🔍
      - Sparse Autoencoders 🎭
   
   3. Network Analysis 🕵️
      - Induction Heads 👀
      - Attention Mechanisms 🎪
      - Circuit Analysis 🔌
      - Feature Visualization 📈
      - Concept Directions 🎳

4. Future AI Developments 🚀
   1. AGI Timeline ⏰
      - 2026-2027 Capability Projections 📅
      - Hardware Scaling 💾
      - Data Limitations 📉
      - Geopolitical Factors 🗺️
   
   2. Integration Fields 🎡
      - Biology Research 🔮
      - Drug Discovery 💊
      - Clinical Trials 🏥
      - Programming Automation 🤹
      - Scientific Research 🧮

5. Industry Best Practices 💎
   1. Team Building 🏢
      - Talent Density Focus 👥
      - Mission Alignment 🎪
      - Rapid Scaling Management 📊
      - Culture Development 🌟
   
   2. Research Qualities 🎓
      - Scientific Mindset 🧭
      - Experimental Approach 🗝️
      - Unconventional Thinking 💫
      - Rapid Testing ⚖️
   
   3. Safety Standards 🛡️
      - Model Specifications 📋
      - Behavioral Guidelines 🎮
      - Ethics Implementation ⛑️
      - Industry Collaboration 🤲

6. Emerging Research Areas 🔮
   1. Technical Focus 🎯
      - Long Horizon Learning ⏳
      - Multi-agent Systems 👾
      - Evaluation Systems 📌
      - Interpretability Research 🔭
   
   2. Applications 💼
      - Automated Science 🧫
      - AI Programming Tools ⌨️
      - Biological Simulation 🧯
      - Clinical Applications 💉

7. Model Intelligence 🧿
   1. System Development 🎪
      - Prompt Engineering 📝
      - Response Patterns ♟️
      - Behavioral Modification 🎹
      - Character Development 🎪
   
   2. User Interaction 🎭
      - Autonomy Respect 🎪
      - Safety Boundaries 🔒
      - Communication Adaptation 🗣️
      - Performance Optimization 🎢

"""




# Define the markdown variables
Boxing_and_MMA_Commentary_and_Knowledge = """
# Boxing and UFC Study of 1971 - 2024 The Greatest Fights History

1. In Boxing, the most heart breaking fight in Boxing was the Boom Boom Mancini fight with Duku Kim.
2. After changes to Boxing made it more safe due to the heart break.
3. Rehydration of the brain after weight ins loss preparation for a match is life saving change.
4. Fighting went from 15 rounds to 12.

# UFC By Contrast..
1. 5 Rounds of 5 Minutes each.
2. Greatest UFC Fighters:
    - Jon Jones could be the greatest of all time (GOAT) since he never lost.
    - George St. Pierre
    - BJ Penn
    - Anderson Silva
    - Mighty Mouse MMA's heart at 125 pounds
    - Kabib retired 29 and 0
    - Fedor Milliano
    - Alex Pereira
    - James Tony
    - Randy Couture
3. You have to Judge them in their Championship Peak
4. Chris Weidman
5. Connor McGregor
6. Leg Breaking - Shin calcification and breaking baseball bats

# References:
1. Joe Rogan - Interview #2219
2. Donald J Trump
"""

Multiplayer_Custom_Hosting_Game_Servers_For_Simulated_Worlds = """
# Multiplayer Simulated Worlds

# Farming Simulator 25 Prompt Features with Emojis:
# Top Multiplayer and MMO Games 2024

## 1. Top Multiplayer Survival & Simulation Games 2024 🎮
### 1.1 Survival Games 🏹
- **Rust** 🦾
  * Advanced Base Building Physics
  * Electricity & Automation Systems
  * Dynamic Player-driven Economy
      
- **ARK: Survival Evolved** 🦖
  * Dinosaur Taming & Breeding
  * Tek Tier Technology System
  * Cross-map Resource Networks
      
- **Valheim** ⚔️
  * Norse Mythology Building System
  * Boss-progression World Evolution
  * Structural Integrity Physics
      
- **DayZ** 🧟
  * Realistic Medical System
  * Dynamic Disease Mechanics
  * Advanced Ballistics Simulation
      
- **7 Days to Die** 🏰
  * Voxel Destruction Physics
  * Dynamic Horde AI System
  * Advanced Base Engineering

### 1.2 Simulation & Building Games 🏗️
- **Satisfactory** 🏭
  * 3D Factory Automation
  * Vertical Building Systems
  * Multi-tier Production Chains
      
- **Factorio** ⚙️
  * Complex Logistics Networks
  * Modular Factory Design
  * Advanced Train Systems
      
- **Space Engineers** 🚀
  * Physics-based Construction
  * Programmable Block System
  * Zero-G Engineering

- **Farming Simulator 22** 🚜
  * Real Brand Machinery
  * Complex Production Chains
  * Season-based Agriculture

- **Eco** 🌍
  * Economic Simulation
  * Environmental Impact System
  * Government Creation Tools

## 2. Top MMO Games 2024 🌐
### 2.1 Fantasy MMORPGs 🗡️
- **Final Fantasy XIV** ✨
  * Job System Flexibility
  * Story-driven Content
  * Cross-platform Raids
      
- **World of Warcraft** 🐲
  * Dragonflight Flying System
  * Mythic+ Challenge System
  * Cross-faction Activities
      
- **Elder Scrolls Online** 🏹
  * One Tamriel Level Scaling
  * Housing Construction
  * Champion Point System
      
- **Lost Ark** ⚔️
  * Combat Skill System
  * Island Content System
  * Legion Raid Mechanics
      
- **Black Desert Online** 🎭
  * Action Combat System
  * Life Skill Systems
  * Node Management

### 2.2 Modern/Sci-Fi MMOs 🛸
- **Destiny 2** 👽
  * Buildcrafting System
  * Raid Mechanics
  * Season Narrative Structure
      
- **Star Wars: The Old Republic** 🌟
  * Story Choice System
  * Legacy System
  * Companion Influence
      
- **Warframe** 🤖
  * Movement System
  * Frame Customization
  * Open World Integration
      
- **The Division 2** 🏙️
  * Cover Combat System
  * Dark Zone Mechanics
  * Recalibration System
      
- **Path of Exile** ⚡
  * Skill Gem System
  * Passive Tree Complexity
  * League Mechanics

## 3. Notable Crossplay Games 🎯
- **Minecraft** 📦
  * Cross-platform Building
  * Redstone Engineering
  * Modded Servers

- **Sea of Thieves** 🏴‍☠️
  * Ship Combat Physics
  * Crew Coordination
  * World Events

- **No Man's Sky** 🪐
  * Procedural Planets
  * Base Building Network
  * Multiplayer Expeditions
"""

def get_display_name(filename):
    """Extract text from parentheses or return filename as is."""
    match = re.search(r'\((.*?)\)', filename)
    if match:
        return match.group(1)
    return filename

def get_time_display(filename):
    """Extract just the time portion from the filename."""
    time_match = re.match(r'(\d{2}\d{2}[AP]M)', filename)
    if time_match:
        return time_match.group(1)
    return filename

def sanitize_filename(text):
    """Create a safe filename from text while preserving spaces."""
    # First replace unsafe characters with spaces
    safe_text = re.sub(r'[^\w\s-]', ' ', text)
    # Remove any multiple spaces
    safe_text = re.sub(r'\s+', ' ', safe_text)
    # Trim leading/trailing spaces
    safe_text = safe_text.strip()
    return safe_text[:50]  # Limit length to 50 chars

def generate_timestamp_filename(query):
    """Generate filename with format: 1103AM 11032024 (Query).md"""
    # Get current time in Central timezone
    central = pytz.timezone('US/Central')
    current_time = datetime.now(central)
    
    # Format the timestamp parts
    time_str = current_time.strftime("%I%M%p")  # 1103AM format
    date_str = current_time.strftime("%m%d%Y")  # 11032024 format
    
    # Clean up the query for filename - now preserving spaces
    safe_query = sanitize_filename(query)
    
    # Construct filename: "1103AM 11032024 (Input with spaces).md"
    filename = f"{time_str} {date_str} ({safe_query}).md"
    
    return filename

def delete_file(file_path):
    """Delete a file and return success status."""
    try:
        os.remove(file_path)
        return True
    except Exception as e:
        st.error(f"Error deleting file: {e}")
        return False

def save_ai_interaction(query, ai_result, is_rerun=False):
    """Save AI interaction to a markdown file with new filename format."""
    filename = generate_timestamp_filename(query)
    
    # Format the content differently for rerun vs normal query
    if is_rerun:
        content = f"""# Rerun Query
Original file content used for rerun:

{query}

# AI Response (Fun Version)
{ai_result}
"""
    else:
        content = f"""# Query: {query}

## AI Response
{ai_result}
"""
    
    # Save to file
    try:
        with open(filename, 'w', encoding='utf-8') as f:
            f.write(content)
        return filename
    except Exception as e:
        st.error(f"Error saving file: {e}")
        return None

def get_file_download_link(file_path):
    """Generate a base64 download link for a file."""
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            content = f.read()
        b64 = base64.b64encode(content.encode()).decode()
        filename = os.path.basename(file_path)
        return f'<a href="data:text/markdown;base64,{b64}" download="{filename}">{get_display_name(filename)}</a>'
    except Exception as e:
        st.error(f"Error creating download link: {e}")
        return None

def extract_terms(markdown_text):
    """Parse markdown text and extract terms."""
    lines = markdown_text.strip().split('\n')
    terms = []
    for line in lines:
        line = re.sub(r'^[#*\->\d\.\s]+', '', line).strip()
        if line:
            terms.append(line)
    return terms

def display_terms_with_links(terms):
    """Display terms with various search links."""
    search_urls = {
        "🚀🌌ArXiv": lambda k: f"/?q={quote(k)}",
        "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
        "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}",
        "▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
        "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}",
        "🐦": lambda k: f"https://twitter.com/search?q={quote(k)}",
    }
    for term in terms:
        links_md = ' '.join([f"[{emoji}]({url(term)})" for emoji, url in search_urls.items()])
        st.markdown(f"- **{term}** {links_md}", unsafe_allow_html=True)

def perform_ai_lookup(query):
    """Perform AI lookup using Gradio client."""
    st.write("Performing AI Lookup...")
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    result1 = client.predict(
        prompt=query,
        llm_model_picked="mistralai/Mixtral-8x7B-Instruct-v0.1",
        stream_outputs=True,
        api_name="/ask_llm"
    )
    st.markdown("### Mixtral-8x7B-Instruct-v0.1 Result")
    st.markdown(result1)
    result2 = client.predict(
        prompt=query,
        llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
        stream_outputs=True,
        api_name="/ask_llm"
    )
    st.markdown("### Mistral-7B-Instruct-v0.2 Result")
    st.markdown(result2)
    combined_result = f"{result1}\n\n{result2}"
    return combined_result

def display_file_content(file_path):
    """Display file content with editing capabilities."""
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            content = f.read()
        
        if st.session_state.view_mode == 'view':
            # Display as markdown when viewing
            st.markdown(content)
        else:
            # Edit functionality
            edited_content = st.text_area(
                "Edit content",
                content,
                height=400,
                key=f"edit_{os.path.basename(file_path)}"
            )
            
            if st.button("Save Changes", key=f"save_{os.path.basename(file_path)}"):
                try:
                    with open(file_path, 'w', encoding='utf-8') as f:
                        f.write(edited_content)
                    st.success(f"Successfully saved changes to {file_path}")
                except Exception as e:
                    st.error(f"Error saving changes: {e}")
    except Exception as e:
        st.error(f"Error reading file: {e}")

def file_management_sidebar():
    """Redesigned sidebar with improved layout and additional functionality."""
    st.sidebar.title("📁 File Management")

    # Get list of .md files excluding README.md
    md_files = [file for file in glob.glob("*.md") if file.lower() != 'readme.md']
    md_files.sort()
    st.session_state.files = md_files
    
    if md_files:
        st.sidebar.markdown("### Saved Files")
        for idx, file in enumerate(md_files):
            st.sidebar.markdown("---")  # Separator between files
            
            # Display time
            st.sidebar.text(get_time_display(file))
            
            # Display download link with simplified text
            download_link = get_file_download_link(file)
            if download_link:
                st.sidebar.markdown(download_link, unsafe_allow_html=True)
            
            # Action buttons in a row
            col1, col2, col3, col4 = st.sidebar.columns(4)
            
            with col1:
                if st.button("📄 View", key=f"view_{idx}"):
                    st.session_state.selected_file = file
                    st.session_state.view_mode = 'view'
            
            with col2:
                if st.button("✏️ Edit", key=f"edit_{idx}"):
                    st.session_state.selected_file = file
                    st.session_state.view_mode = 'edit'
            
            with col3:
                if st.button("🔄 Rerun", key=f"rerun_{idx}"):
                    try:
                        with open(file, 'r', encoding='utf-8') as f:
                            content = f.read()
                            
                        # Prepare the prompt with the prefix
                        rerun_prefix = """For the markdown below reduce the text to a humorous fun outline with emojis and markdown outline levels in outline that convey all the facts and adds wise quotes and funny statements to engage the reader:

"""
                        full_prompt = rerun_prefix + content
                        
                        # Perform AI lookup and save results
                        ai_result = perform_ai_lookup(full_prompt)
                        saved_file = save_ai_interaction(content, ai_result, is_rerun=True)
                        
                        if saved_file:
                            st.success(f"Created fun version in {saved_file}")
                            st.session_state.selected_file = saved_file
                            st.session_state.view_mode = 'view'
                            
                    except Exception as e:
                        st.error(f"Error during rerun: {e}")
            
            with col4:
                if st.button("🗑️ Delete", key=f"delete_{idx}"):
                    if delete_file(file):
                        st.success(f"Deleted {file}")
                        st.rerun()
                    else:
                        st.error(f"Failed to delete {file}")

        st.sidebar.markdown("---")
        # Option to create a new markdown file
        if st.sidebar.button("📝 Create New Note"):
            filename = generate_timestamp_filename("New Note")
            with open(filename, 'w', encoding='utf-8') as f:
                f.write("# New Markdown File\n")
            st.sidebar.success(f"Created: {filename}")
            st.session_state.selected_file = filename
            st.session_state.view_mode = 'edit'
    else:
        st.sidebar.write("No markdown files found.")
        if st.sidebar.button("📝 Create First Note"):
            filename = generate_timestamp_filename("New Note")
            with open(filename, 'w', encoding='utf-8') as f:
                f.write("# New Markdown File\n")
            st.sidebar.success(f"Created: {filename}")
            st.session_state.selected_file = filename
            st.session_state.view_mode = 'edit'

def main():
    st.title("AI Knowledge Tree Builder 🧠🌱 Cultivate Your AI Mindscape!")

    # Process query parameters and AI lookup first
    query_params = st.query_params
    query = query_params.get('q', '')
    show_initial_content = True  # Flag to control initial content display
    
    # First priority: Handle active query
    if query:
        show_initial_content = False  # Hide initial content when showing query results
        st.write(f"### Search query received: {query}")
        try:
            ai_result = perform_ai_lookup(query)
            
            # Save the interaction
            saved_file = save_ai_interaction(query, ai_result)
            if saved_file:
                st.success(f"Saved interaction to {saved_file}")
                st.session_state.selected_file = saved_file
                st.session_state.view_mode = 'view'
        except Exception as e:
            st.error(f"Error during AI lookup: {e}")

    # File management sidebar
    file_management_sidebar()

    # Second priority: Display selected file content if any
    if st.session_state.selected_file:
        show_initial_content = False  # Hide initial content when showing file content
        if os.path.exists(st.session_state.selected_file):
            st.markdown(f"### Current File: {st.session_state.selected_file}")
            display_file_content(st.session_state.selected_file)
        else:
            st.error("Selected file no longer exists.")
            st.session_state.selected_file = None
            st.rerun()

# Show initial content: Either when first landing or when no interactive elements are active
    if show_initial_content:
        # First show the clickable terms with links
        terms1 = extract_terms(AITopicsToInnovate1)
        terms2 = extract_terms(Multiplayer_Custom_Hosting_Game_Servers_For_Simulated_Worlds)
        all_terms = terms1 + terms2

        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            st.markdown("# AI Topics to Innovate With")
            st.markdown(AITopicsToInnovate1)
        with col2:
            st.markdown("# AI Agent Links")
            display_terms_with_links(terms1)
        with col3:
            st.markdown("# Multiplayer Games and MMOs")
            st.markdown(Multiplayer_Custom_Hosting_Game_Servers_For_Simulated_Worlds)
        with col4:
            st.markdown("# Multiplayer Game and MMO Links
            display_terms_with_links(terms2)

if __name__ == "__main__":
    main()